COMPARISON OF MAIN EFFECT OF VEGETABLE OIL AND AL 2 O 3 NANOFLUIDS USED WITH MQL ON SURFACE ROUGHNESS AND TEMPERATURE

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 7, Issue 1, Jan-Feb 2016, pp , Article ID: IJMET_07_01_021 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication COMPARISON OF MAIN EFFECT OF VEGETABLE OIL AND AL 2 O 3 NANOFLUIDS USED WITH MQL ON SURFACE ROUGHNESS AND TEMPERATURE Shrikant Borade Master of Manufacturing Engineering Student, Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra, , India Dr.M.S.Kadam Professor and Head of Department, Mechanical Engineering, Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra, , India ABSTRACT The growing demands for high productivity of machining need use of high cutting velocity and feed rate. Such machining inherently produces high cutting temperature, which not only reduces tool life but also impairs the product quality. Application of cutting fluids changes the performance of machining operations because of their lubrication, cooling, and chip flushing functions. But the conventional cutting fluids are not that effective in such high production machining, particularly in continuous cutting of materials likes steels. So Nanofluids have novel properties that make them potentially useful in heat transfer medium in cutting zone and Minimum quantity lubrication (MQL) presents itself as a viable alternative for turning with respect to tool wear, heat dissipation, and machined surface quality. In this study Vegetable oil with MQL is compared with Al 2 O 3 Nanfluids and MQL for the turning of EN353 steel based on experimental measurement of surface roughness and temperature. This study prepares the effect of MQL and Nano fluids with 3% volume Al 2 O 3 on the machinability characteristics of EN353 steel mainly with respect to surface roughness and temperature dissipation. Experimental analysis for two different conditions MQL + Vegetable Oil and MQL + Al 2 O 3 Nanofluids were carried out. Key words: Al 2 O 3 Nanofluids, MQL, CNC Machining, EN353 steel, Surface Roughness, Temperature editor@iaeme.com

2 Shrikant Borade and Dr.M.S.Kadam Cite this Article: Shrikant Borade and Dr.M.S.Kadam. Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on Surface Roughness and Temperature. Dynamical Analysis of Silo Surface Cleaning Robot Using Finite Element Method, International Journal of Mechanical Engineering and Technology, 7(1), 2016, pp INTRODUCTION Cutting fluids are employed in machining operations to improve the tribological processes, which occur when two surfaces, the tool and work-piece make contact. The cutting fluid improves the tool life, surface conditions of the work-piece and the process as a whole. It also helps in carrying away the heat and debris produced during machining. On the other hand, the cutting fluids have many detrimental effects. Machining experiences high temperatures due to friction between the tool and work piece, thus influencing the work piece dimensional accuracy and surface quality. Machining temperatures can be controlled by reducing the friction between tool work piece and tool chip interface with the help of effective lubrication. Cutting fluids are the conventional choice to act as both lubricants and coolants. But, their application has several adverse effects such as environmental pollution, dermatitis to operators, water pollution and oil contamination during disposal [1,2]. Further, the cutting fluids also incur a major portion of the total manufacturing cost. All these factors prompted investigations into the use of biodegradable coolants or coolant free machining. Hence, as an alternative to cutting fluids, researchers experimented with dry machining, coated tools, cryogenic cooling, minimum quantity lubrication (MQL) and solid lubricants. Dry machining, has been reported to be capable of eliminating cutting fluids with the advancement of the cutting tool materials [3]. Dry machining requires less power and produces smoother surface than wet machining at specified cutting conditions [4]. In tribological applications of hard protective coatings the toughness is as important as their hardness, since both properties have great influence on the wear resistance of coated tools [5]. Itakuraetal. [6] conducted dry turning experiments to identify the tool wear mechanisms with coated cemented carbide tool while machining in conel 718. During continuous cutting almost no rake wear but only flank wear appeared. The primary function of a cutting fluid in wet machining operations is to cool, to lubricate, and to remove the chips. Emulsions or straight oils are generally used depending on the manufacturing operation and machining task involved. Straight oil is a cutting fluid that is composed of mineral oil or vegetable oil and is mainly used as a lubricant. Research by Rao DN, Srikant RR (2006) showed straight oil is not intended to be mixed with water. Emulsions possess excellent heat transfer characteristics because of their high water content. Straight oils excel when a high degree of lubricity is required. Both media guarantee efficient chip transport, when compressed air is used instead of a cooling lubricant, the lubrication benefit of the fluid is lost. The coolant effect is much less pronounced than with water or oil. Water and oil are also superior to air in terms of chip transport characteristics editor@iaeme.com

3 Comparison of Main Effect of Vegetable Oil and Al 2 o 3 Nanofluids Used with MQL on Surface Roughness and Temperature 2. EXPERIMENTAL SETUP 2.1 Selection of work material The workpiece material used is EN353 steel in the form of round bars of 30 mm diameter and length of 80 mm. The composition of material is Table 1 Composition of EN353 Steel chemical composition of EN 353 Steel C Mn Cr Si Mo Ni 0.18% 0.93% 1.11% 0.26% 0.11% 1.34% EN353 steel is widely used for Machining components in various industries. This material has significant application in automotive industry. Typical applications of this material are crown wheel, crown pinion, bevel pinion, bevel wheel, timing gears, king pin, pinion shaft, differential turn ion etc. The gears especially crown wheel and pinion are one of the most stress prone parts of a vehicle, etc, which are made of EN353 steel. This material EN353 steel is used in heavy duty chains, conveyor chains. The workpiece of EN353 Steel was firstly hardened followed by oil quenching at a temperature of 850 C to achieve a hardness of 60 HRC throughout. A rough turning pass was conducted initially to eliminate the run out of the work piece, after that diameter obtained for experimentation is 60mm Selection of Insert Based on Literature survey, the Tool selected for this process was CBN CNMG (Cubic Boron Nitride) 7025 Manufactured by Sandvik Specifications as CNMG Selection of lubricant Selection of cutting fluid is important in order to maintain better tool life, less cutting forces, lower power consumption, high machining accuracy and better surface integrity etc. Here Vegetable Oil {Max mix ST-2020}, is used as cutting fluid in MQL. ST-2020 is an environmentally acceptable vegetables oil based lubricant. ST will yield the lowest net manufacturing costs of any fluid as found by JungSoo, et al. (2011.) 2.4. Preparation of Nano fluids For preparation of Al 2 O 3 nanoparticles the Precipitation Method is used. Alumina prepared by this method is used for preparation of Nanofluids. In this work the alumina (Al 2 O 3 ) Nanoparticles are mixed with vegetable oil, as a base fluid to make Al 2 O 3 Nanofluid. Nano Al 2 O 3 particles are selected due to their superior tribological and antitoxic properties based on study of Pil-Ho Lee, et al. (2012). The method used to make Nano fluid is given below. Take one gram of Al 2 O 3 Nano particles and directly mix with 100 ml vegetable oil as a base fluid and prepare the sample. 1 Vol% of Al 2 O 3 Nano fluid = 100 ml of vegetable oil + 1gm of Al 2 O 3 Nano particles. 3Vol% of Al 2 O 3 Nano fluid = 100 ml of vegetable oil + 3gm of Al 2 O 3 Nano particles. The above composition has to be mixed continuously about 8 to 9 hours using Magnetic stirrer. The size of Al 2 O 3 particles is about 5 Nm. Here after, Nanofluid means a mix of MQL and Al 2 O 3 particles editor@iaeme.com

4 Shrikant Borade and Dr.M.S.Kadam 2.5. Selection of process parameter Input process parameters were selected after studying the literature on minimum quantity lubrication/near dry machining. Cutting speed (Vc), cutting feed (f) and Depth of cut (dc) were the input parameters chosen for present work. To investigate the effect of input parameters under the application of MQL on EN353 Steel machining technique. The output parameters chosen were Surface Roughness (Ra) and Temperature (T) at cutting zone to investigate the effect of input parameters under the application of MQL for turning on case hardened EN353 steel. The cutting insert used was CNMG7075 insert Machine Figure 1 CNC Lathe (HAAS Make) A high speed precision CNC Lathe (HASS - MAKE) having maximum turning diameter 406mm, Height of centers over carriage 60 mm Maximum turning length: 762mm, Headstock A2-5 / CAMLOCK D1-3 Spindle speed 2,000 rev/min, AC motor drive: Power consumption 9kW, Transverse stroke X-axis 209mm, AC motor drive: intermittent torque 4 to 14 Nm Longitudinal stroke, Z-axis 762mm, Number of fixed tool stations/rotating tool stations. Weight of CNC Lathe 1860kgPower Requirement: 9KVA, 1-Phase:240V@40A, 3-Phase: 208V Table 2 Selected Parameter Sr. no. Parameter Selected Parameter 1 Machine tool: High speed precision CNC Lath (HASS MAKE) 2 Work pieces EN353 Case Hardening material 3 Cutting tool inserts CBN CNMG Cutting speed rpm 5 Feed rate, f: mm/rev 6 Depth of cut,(d) mm 7 MQL supply: Air:4 bar, Lubricant: 50 ml/h 8 Environment: MQL with Vegetable oil and MQL with Nanofluids. 9 Measurement of surface roughness Surf Tester 10 Measurement of cutting temperature Least Count Infrared thermometer Temperature Range- -40 C to 400 C Least Count - 1 C editor@iaeme.com

5 Comparison of Main Effect of Vegetable Oil and Al 2 o 3 Nanofluids Used with MQL on Surface Roughness and Temperature 2.6. Experimental procedure The experiments were performed on the basis of Taguchi s L9 array method. By performing One variable at time (OVAT) analysis and from graph it is found that cutting speed, feed rate, depth of cut are influencing parameters on Surface Roughness and Cutting temperature. According to OVAT analysis following input parameters namely cutting speed, feed rate and depth of cut are selected by keeping other process parameters constant at minimum level which is less influencing on Surface roughness and Cutting temperature [15]. On the basis of surface finish, the selected levels of input parameters which are as follows. Table 3 Selection of Levels from OVAT Parameters Units Level 1 Level 2 Level 3 Speed rpm Feed mm/rev DOC mm RESULTS AND DISCUSSION 3.1. For Vegetable Oil with MQL For Surface Roughness Table no.5 and Figure no.2 indicates the signal to noise ratio. In this table highest signal to noise ratio is indicated in bold format. This higher signal to noise ratio indicates that, this particular input is favourable on the output. From the graph it is observed that for best surface roughness the optimum value levels are Speed 800 rpm, Feed 0.05 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing parameter from these three is Depth of cut then followed by feed and then speed. Analysis of variance (ANOVA) Table 4 Analysis of Variance for Signal to Noise Ratios Level Speed Feed Depth of Cut Delta Rank Response Table for Signal to Noise Ratios (Smaller is better) Table 5 Signal to Noise Ratios - Smaller is better Source DF Seg SS Adj SS Adj MS F P S F D Residual Error Total Source DF Seg SS Adj SS Adj MS F P S F D Residual Error Total editor@iaeme.com

6 Mean of SN ratios Shrikant Borade and Dr.M.S.Kadam Main Effect Plot for Signal to Noise Ratios 9 Main Effects Plot (data means) for SN ratios S F D Signal-to-noise: Smaller is better Figure 2 Main Effect Plot for SIGNAL TO NOISE ratios For Temperature ( o C) Table no.7 and Figure no.2 indicates the signal to noise ratio. In this both the table highest signal to noise ratio is indicated in bold format. This higher signal to noise ratio indicates that, this particular input is favourable on the output. From the graph it is observed that for best temperature the optimum value levels are Speed 800 rpm, Feed 0.05 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing parameter from these three is Depth of cut then followed by feed and at last speed. Analysis of variance (ANOVA) Table 6 Analysis of Variance for Signal to Noise Ratios Source DF Seg SS Adj SS Adj MS F P S F D Residual Error Total Response Table for Signal to Noise Ratios (Smaller is better) Table 7 Signal to Noise Ratios - Smaller is better Level Speed Feed Depth of Cut Delta Rank editor@iaeme.com

7 Mean of SN ratios Comparison of Main Effect of Vegetable Oil and Al 2 o 3 Nanofluids Used with MQL on Surface Roughness and Temperature Main Effect Plot for Signal to Noise Ratios Main Effects Plot (data means) for SN ratios Speed Feed Depth of Cut Signal-to-noise: Smaller is better 4.2. For Nanofluids with MQL Figure 3 Main Effect Plot for Signal to Noise Ratios For Surface Roughness Table no.8 and Figure no.4 indicates the signal to noise ratio. In this both the table highest signal to noise ratio is indicated in bold format. This higher signal to noise ratio indicates that, this particular input is favourable on the output. From the graph it is observed that for best surface roughness the optimum value levels are Speed 800 rpm, Feed 0.07 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing parameter from these three is Depth of cut then followed by feed and at last speed. Analysis of variance (ANOVA) Table 10 Analysis of Variance for Signal to Noise Ratios Source DF Seg SS Adj SS Adj MS F P S F D Residual Error Total Response Table for signal to Noise Ratios (smaller is better) Table 11 Signal to Noise Ratios - Smaller is better Level Speed Feed Depth of Cut Delta Rank editor@iaeme.com

8 Mean of SN ratios Shrikant Borade and Dr.M.S.Kadam Main Effect Plot for Signal to Noise Ratios 10.8 Main Effects Plot (data means) for SN ratios Speed Feed Depth of Cut Signal-to-noise: Smaller is better Figure 5 Main Effect Plot For Temperature Table no.13 and Figure no.6 indicates the signal to noise ratio. In this both the table highest signal to noise ratio is indicated in bold format. This higher signal to noise ratio indicates that, this particular input is favourable on the output. From the graph it is observed that for best surface roughness the optimum value levels are Speed 800 rpm, Feed 0.07 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing parameter from these three is Depth of cut then followed by feed and at last speed. Analysis of variance (ANOVA) Table 12 Analysis of Variance for Signal to Noise Ratios Source DF Seg SS Adj SS Adj MS F P S F D Residual Error Total Response Table for Signal to Noise Ratios (Smaller is better) Table 13 Signal to Noise Ratios - Smaller is better Level Speed Feed Depth of Cut Delta Rank editor@iaeme.com

9 Comparison of Main Effect of Vegetable Oil and Al 2 o 3 Nanofluids Used with MQL on Surface Roughness and Temperature 4.2. Comparison of Main Effect of Vegetable oil and Al 2 O 3 Nanofluids used with MQL on Surface Roughness and Temperature Surface Roughness (µm) Figure 7 Comparison graph for surface roughness From the figure 7 it is observed that at each experiment the value of surface roughness when used with Al 2 O 3 Nanofluids is comparatively low as compared with Vegetable oil. The smooth surface finish were obtained while using Al 2 O 3 nanofluids with MQL Temperature (C) Figure 8 Comparison graph for surface roughness From the figure 8 it is observed that at each experiment the value of temperature when used with Al 2 O 3 Nanofluids is comparative low as compared with Vegetable oil. The main problem with machining any component is heat generated while machining. To cool down the work piece litres of coolant were wasted. Hence the Al 2 O 3 nanofluid with MQL is better option for flooded cooling system editor@iaeme.com

10 Shrikant Borade and Dr.M.S.Kadam 6. ACKNOWLEDGEMENTS The authors would like to thank Principal Dr. Sudhir Deshmukh MGM s JNEC Aurangabad, for continuously assessing my work providing great guidance by timely suggestion and discussion at ever stages of this work. We would like to thanks all Mechanical Engineering Department MGM s JNEC Aurangabad. The authors would also like to thank Indo German Tool Room, Aurangabad and Swajit Engineering Pvt.Ltd, Aurangabad for significant information and kind support for experimentation and making available their testing facility for Research Work. 6. CONCLUSIONS The present work has successfully demonstrated Al 2 O 3 Nano fluid applications in CNC turning on EN353 steel using Minimum Quantity Lubrication. The surface roughness (Ra) and Temperature (T) were measured under different cutting conditions for diverse combinations of machining parameters. The final conclusions arrived, at the end of this work are as follows: These experiments show that Surface Roughness and Temperature reduced significantly by machining EN353 steel using Nanofluid (i.e. 3 Vol. % of Al 2 O 3 Nano fluid) as compared to MQL Vegetable oil. The percentage of error between the predicted and experimental values of the multiple performance characteristics during the confirmation experiments is less than 5 % as it is within limit. The value of multiple performance characteristics obtained from confirmation experiment is within the 91% to 91.30% confidence interval of the predicted optimum condition. Hence it is concluded that Depth of Cut has significant effect on surface roughness and temperature when used with MQL + Nanofluids. Followed by Feed, Speed in Surface Roughness and Speed, Feed in Temperature. REFERENCES [1] Anselmo Eduardo Diniz, Ricardo Micaroni, (2002), Cutting conditions for finish turning process aiming: the use of dry Cutting, International Journal of Machine Tools & Manufacture 42, [2] Buongiorno, J. (March 2006), Convective Transport in Nanofluids, Journal of Heat Transfer (American Society of Mechanical Engineers) 128 (3): 240. Retrieved 27 March [3] Douglas C. Montgomery (2009), Design and Analysis of Experiments, 7th edition Wiley. [4] E.O. Ezugwua,R.B. Da Silvaa,J. Bonneya, Á.R.Machadob, (2005), International Journal of Machine Tools and Manufacture, Volume 45, Issue 9, Pages [5] EmelKuram, Babur Ozcelik and ErhanDemirbas (2013), Environmentally Friendly Machining: Vegetable Based Cutting Fluids, Green Manufacturing Processes and Systems, Materials Forming, Machining and Tribology, [6] Eastman J A, Phillpot S R, Choi S U S, and Keblinski P, (2004), Thermal transport in nanofluids, A Rev. Mater. Res., 34, [7] Funda Kahraman,(2009), The use of Response Surface Methodology for Prediction and Analysis of Surface Roughness of AISI 4140 Steel, Materiali in tehnologije / Materials and technology, editor@iaeme.com

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